1. Field of the Invention
The invention relates to a speech dialogue processing technique. More particularly, the invention relates to a natural language dialogue system and a method capable of correcting a speech response.
2. Description of Related Art
In the field of natural language recognitions, computers usually use certain syntax to capture and recognize user's intentions or information within his/her inputs. Accordingly, computers are able to determine user's intentions if there are sufficient data relating to sentences input by users stored in databases of the computers.
Conventionally, a built-in phrase list including specific idioms indicating certain intentions or information is often applied to compare with user sentences, and every user is asked to express his/her intentions with the uses of the well-defined specific idioms within the phrase list, such that his/her intentions may be correctly recognized by computers. However, it is rather unreasonable and/or unpractical if the user is forced to remember every idiom included in the phrase list. For instance, if a user intends to learn the weather conditions, he/she may be asked to input “what is the weather tomorrow (or the day after tomorrow) in Shanghai (or Beijing)”? In case the user uses another colloquial expression instead, e.g., “how is Shanghai tomorrow?”, this expression may be interpreted as “there is a place called ‘tomorrow’ in Shanghai” because the word “weather” is not shown in his/her sentence. Thereby, the user's intention may be misunderstood by computers. In addition, user's sentences are usually complicated and diverse, and sometimes his/her inputs may be erroneous, which needs fuzzy matching processes for further identifications. Obviously, those phrase lists established under this conventionally rigid input-rule usually conduct disappointing analysis results.
From another perspective, one syntactic structure/sentence may refer to different intentions even if all possible principles of natural language analyses are applied to recognize users' intentions. For instance, if the user sentence is “I want to see the Romance of the Three Kingdoms”, he/she may intend to watch the film of “Romance of the Three Kingdoms” or read the book of “Romance of the Three Kingdoms”. Under such a scenario, the user has to make a further selection between these two matches. Sometimes, it will be redundant and inefficient for a user to make selection among meaningless matches. For instance, if a user's sentence is “I want to see One Million Star”, it is unnecessary to recognize the user's intention as a book or a painting of “One Million Star” (because “One Million Star” is a very famous TV show among Chinese).
Moreover, in most cases, search results obtained from a full-text search are non-structured data, which usually contains separate and unrelated information therein. For instance, if a user inputs a keyword in a search engine (e.g., Google or Baidu) for searches, search results in webpages usually include separate and diverse information waiting for user's identifications. The only way for the user to find out useful information contained in the search results is to browse and/or look into those webpages one-by-one. It is really a time-cost approach for the user to browse those search results, and, sometimes, he/she may skip or miss his/her desired information inadvertently. The uses of the search results obtained conventionally are accordingly limited.